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From: geoff@cs.miami.edu
To: <caml-list@inria.fr>
Subject: [Caml-list] 5th Workshop on Bridging the Gap between Human and Automated Reasoning
Date: Thu,  7 Mar 2019 08:59:28 -0500 (EST)
Message-ID: <20190307135928.72F241700E61@cs.miami.edu> (raw)

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CALL FOR PAPERS
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[Please kindly help forward it to potentially interested attendees]


Fifth Workshop on:
Bridging the Gap between Human and Automated Reasoning
an IJCAI-19 workshop (supported by IFIP TC12)
Macau, China August, 2019
http://ratiolog.uni-koblenz.de/bridging2019

=================================================================

Reasoning is a core ability in human cognition. Its power lies in the 
ability to theorize about the environment, to make implicit knowledge 
explicit, to generalize given knowledge and to gain new insights. 
There are a lot of findings in cognitive science research which are 
based on experimental data about reasoning tasks, among others models 
for the Wason selection task  or the suppression task discussed by 
Byrne and others. This research is supported also by brain researchers, 
who aim at localizing reasoning processes within the brain.

Early work often used propositional logic as a normative framework. Any 
deviation from it has been considered an error. Central results like 
findings from the Wason selection task or the suppression task inspired 
a shift from propositional logic and the assumption of monotonicity in 
human reasoning towards other reasoning approaches. This includes but is 
not limited to models using probabilistic approaches, mental models, or 
non-monotonic logics. Considering  cognitive theories for syllogistic 
reasoning show that none of the existing theories is close to the existing 
data. But some formally inspired cognitive complexity measures can predict 
human reasoning difficulty for instance in spatial relational reasoning.

Automated deduction, on the other hand, is mainly focusing on the 
automated proof search in logical calculi. And indeed there is tremendous 
success during the last decades. Recently a coupling of the areas of 
cognitive science and automated reasoning is addressed in several 
approaches. For example there is increasing interest in modeling human 
reasoning within automated reasoning systems including modeling with 
answer set programming, deontic logic or abductive logic programming. 
There are also various approaches within AI research for commonsense 
reasoning and in the meantime there even exist benchmarks for commonsense 
reasoning, like the Winograd and the COPA challenge. 

A core goal of Bridging-the-gap-Workshops is to make results from 
psychology, cognitive science, and AI accessible to each other. The 
goal is to develop systems that can adapt themselves to an individuals' 
reasoning process and that such systems follow the principle of explainable 
AI to ensure trustfulness and to support the integration of results from 
other fields. We propose a human syllogistic reasoning challenge to 
predict future inferences of an individual reasoner based on some previous 
observations. Hence, participants can develop cognitive AI models (written 
in Python) that predict the next inference. These predictions are then 
evaluated in the CCobra framework (for more information see 
https://www.cognitive-computation.uni-freiburg.de/modelingchallenge).

Despite a common research interest -- reasoning -- there are still 
several milestones necessary to foster a better inter-disciplinary 
research. First, to develop a better understanding of methods, techniques, 
and approaches applied in both research fields. Second, to have a synopsis 
of the relevant state-of-the-art in both research directions. Third, to 
combine methods and techniques from both fields and find synergies. E.g., 
techniques and methods from computational logic have never been directly 
applied to model adequately human reasoning. They have always been 
adapted and changed. Fourth, we need more and better experimental data 
that can be used as a benchmark system. Fifth, cognitive theories can 
benefit from a computational modeling. Hence, both fields -- human 
and automated reasoning -- can both contribute to these milestones and 
are in fact a conditio sine qua non. Achievements in both fields can 
inform the others. Deviations between fields can inspire to seek a new 
and profound understanding of the nature of reasoning. Additionally to 
predict human inferences is a major step that can help to foster the 
integration of digital companions and cognitive assistance systems into 
our everyday life. An important condition is that such systems can adapt 
themselves to an individual's reasoning process and that such systems 
follow the principle of explainable AI to ensure trustfulness and to 
support the integration of results from other fields. Symbolic approaches 
do provide an easier access to it.  

This is the fifth workshop in a series of successful Bridging the Gap 
Between Human and Automated Reasoning workshops. 

Topics of interest include, but are not limited to the following:

- limits and differences between automated and human reasoning 
- psychology of deduction and common sense reasoning 
- logics modeling human reasoning 
- non-monotonic, defeasible, and classical reasoning 
- benchmark problems relevant in both fields 
- approaches to tackle benchmark problems like the Winograd Schema 
  Challenge or the COPA challenge 
- predicting an individual reasoners response (see 
  https://www.cognitive-computation.uni-freiburg.de/modelingchallenge)
	
The workshop will be located at the 28th International Joint Conference 
on Artificial Intelligence (IJCAI 2019) at Macao, China. The Bridging 
workshop is supported by IFIP TC12.


======== IMPORTANT DATES ========
Full Paper submission deadline: 12th April, 2019
Notification: 10th May, 2019
Final submission: 10th June, 2019
Model submission for PRECORE challenge: 15th May, 2019
Workshop: 10th - 12th August, 2019


======== SUBMISSION AND CONTRIBUTION FORMAT ========
This year's Bridging workshop will accept papers and submissions to 
the PRECORE challenge:

Papers, including the description of work in progress, are welcome and 
should be formatted according to the Springer LNCS guidelines. The length 
should not exceed 15 pages. All papers must be submitted in PDF. 
Formatting instructions and the LNCS style files can be obtained at 
http://www.springer.de/comp/lncs/authors.htm. 
The EasyChair submission site is available at:   
https://easychair.org/conferences/?conf=bridging2019

The PRECORE challenge is based on CCOBRA 
(https: //www.cognitive-computation.uni-freiburg.de/modelingchallenge), 
a Python framework for the behavioral analysis of reasoning models. 
The framework does not pose restrictions with respect to formalisms as 
long as individual predictions to syllogistic problems can be generated. 
Final model submissions are due on May 15th, 11:59 UTC-12 as a zip-archive. 
Please describe your model on a conceptual level on two pages in the 
workshop template. Details on the submission of the zip-archive can be 
found at: 
https://www.cognitive-computation.uni-freiburg.de/modelingchallenge

======== PROCEEDINGS========
Proceedings of the workshop will probably be published as CEUR workshop 
proceedings. 

======== ORGANIZERS ======== 
Ulrich Furbach, University of Koblenz
Steffen Hölldobler, University of Dresden
Marco Ragni, University of Freiburg
Claudia Schon, University of Koblenz


======== PROGRAM COMMITTEE ========
Christoph Beierle, Fernuniversität Hagen
Phan Minh Dung, Asian Institute of Technology, Dresden University of Technology
Ulrich Furbach, University of Koblenz
Steffen Hölldobler, University of Dresden
Antonis C. Kakas, University Cyprus
Sangeet Khemlani, Naval Research Lab, USA
Robert A. Kowalski, Imperial College London
Luís Moniz Pereira, Universidade Nova Lisboa
Marco Ragni, University of Freiburg
Nicolas Riesterer,  University of Freiburg
Claudia Schon, University of Koblenz
Frieder Stolzenburg, Harz University of Applied Sciences

Contact: Claudia Schon, schon@uni-koblenz.de

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